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419fc11 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 | from __future__ import annotations
from dataclasses import dataclass
from typing import Any, Dict, List, Tuple
import torch
from PIL import Image
from train.encoders import build_backbones
from train.graph_builder import build_bipartite_batch
from train.graph_models import NativeGNNClassifier
from train.models import BaselineClassifier
@dataclass
class InferenceResult:
labels: List[str]
scores: List[float]
positives: List[str]
def _sorted_labels(
scores: torch.Tensor,
label_vocab: Dict[str, int],
top_k: int,
threshold: float,
) -> InferenceResult:
inv_vocab = {idx: label for label, idx in label_vocab.items()}
probs = scores.sigmoid().detach().cpu().view(-1)
values, indices = torch.topk(probs, k=min(top_k, probs.numel()))
labels = [inv_vocab[int(i)] for i in indices]
positives = [label for label, score in zip(labels, values) if float(score) >= threshold]
return InferenceResult(
labels=labels,
scores=[float(v) for v in values],
positives=positives,
)
class BaseAdapter:
def __init__(self, device: torch.device) -> None:
self.device = device
def predict(
self,
image: Image.Image,
label_vocab: Dict[str, int],
top_k: int = 5,
threshold: float = 0.5,
) -> InferenceResult:
raise NotImplementedError
class BaselineAdapter(BaseAdapter):
def __init__(self, checkpoint: Dict[str, Any], device: torch.device) -> None:
super().__init__(device)
cfg = checkpoint["config"]
num_labels = len(checkpoint["label_vocab"])
use_clip = bool(cfg["encoders"]["clip"]["enabled"])
use_dino = bool(cfg["encoders"]["dino"]["enabled"])
clip_name = str(cfg["encoders"]["clip"].get("model_name", "vit_base_patch16_clip_224.openai"))
dino_name = str(cfg["encoders"]["dino"].get("model_name", "vit_base_patch14_dinov2.lvd142m"))
if clip_name == "ViT-B-32":
clip_name = "vit_base_patch16_clip_224.openai"
if dino_name == "vit_base_patch14_dinov2":
dino_name = "vit_base_patch14_dinov2.lvd142m"
clip_backbone, dino_backbone, feature_dim = build_backbones(
use_clip=use_clip,
use_dino=use_dino,
clip_model_name=clip_name,
dino_model_name=dino_name,
clip_pretrained=bool(cfg["encoders"]["clip"].get("pretrained", True)),
dino_pretrained=bool(cfg["encoders"]["dino"].get("pretrained", True)),
)
hidden_dims = list(cfg["baseline_heads"]["mlp"].get("hidden_dims", [512, 256]))
dropout = float(cfg["baseline_heads"]["mlp"].get("dropout", 0.2))
model = BaselineClassifier(
clip_backbone=clip_backbone,
dino_backbone=dino_backbone,
feature_dim=feature_dim,
hidden_dims=hidden_dims,
dropout=dropout,
num_labels=num_labels,
)
model.load_state_dict(checkpoint["model_state"])
model.eval().to(device)
self.model = model
def predict(
self,
image: Image.Image,
label_vocab: Dict[str, int],
top_k: int = 5,
threshold: float = 0.5,
) -> InferenceResult:
with torch.no_grad():
logits = self.model([image], device=self.device)[0]
return _sorted_labels(logits, label_vocab, top_k, threshold)
class GNNAdapter(BaseAdapter):
def __init__(self, checkpoint: Dict[str, Any], device: torch.device) -> None:
super().__init__(device)
cfg = checkpoint["config"]
num_labels = len(checkpoint["label_vocab"])
use_clip = bool(cfg["encoders"]["clip"]["enabled"])
use_dino = bool(cfg["encoders"]["dino"]["enabled"])
clip_name = str(cfg["encoders"]["clip"].get("model_name", "vit_base_patch16_clip_224.openai"))
dino_name = str(cfg["encoders"]["dino"].get("model_name", "vit_base_patch14_dinov2.lvd142m"))
if clip_name == "ViT-B-32":
clip_name = "vit_base_patch16_clip_224.openai"
if dino_name == "vit_base_patch14_dinov2":
dino_name = "vit_base_patch14_dinov2.lvd142m"
clip_backbone, dino_backbone, feature_dim = build_backbones(
use_clip=use_clip,
use_dino=use_dino,
clip_model_name=clip_name,
dino_model_name=dino_name,
clip_pretrained=bool(cfg["encoders"]["clip"].get("pretrained", True)),
dino_pretrained=bool(cfg["encoders"]["dino"].get("pretrained", True)),
)
class BackboneWrapper(torch.nn.Module):
def __init__(self, clip_module: torch.nn.Module | None, dino_module: torch.nn.Module | None) -> None:
super().__init__()
self.clip_module = clip_module
self.dino_module = dino_module
def encode(self, images: List[Image.Image], device: torch.device) -> torch.Tensor:
feats: List[torch.Tensor] = []
if self.clip_module is not None:
feats.append(self.clip_module.forward_pil(images, device=device))
if self.dino_module is not None:
feats.append(self.dino_module.forward_pil(images, device=device))
if len(feats) == 1:
return feats[0]
return torch.cat(feats, dim=1)
backbone = BackboneWrapper(clip_backbone, dino_backbone).to(device)
self.backbone = backbone
hidden_dims = [layer_cfg["out_dim"] for layer_cfg in cfg["gnn"]["layers"]]
dropout = float(cfg["gnn"].get("dropout", 0.2))
gnn_model = NativeGNNClassifier(
in_dim=feature_dim,
hidden_dims=hidden_dims,
num_attributes=num_labels,
dropout=dropout,
)
gnn_model.load_state_dict(checkpoint["gnn_state"])
gnn_model.eval().to(device)
self.gnn_model = gnn_model
def predict(
self,
image: Image.Image,
label_vocab: Dict[str, int],
top_k: int = 5,
threshold: float = 0.5,
) -> InferenceResult:
with torch.no_grad():
feats = self.backbone.encode([image], device=self.device).unsqueeze(1)
# Single-sample multi-hot target is unknown at inference; use zeros to build graph shape.
dummy_targets = torch.zeros(1, len(label_vocab), device=self.device)
graph = build_bipartite_batch(feats=feats, targets=dummy_targets)
logits = self.gnn_model(graph)[0]
return _sorted_labels(logits, label_vocab, top_k, threshold)
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